
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDeiTForImageClassification,
        TFDeiTForImageClassificationWithTeacher,
        TFDeiTForMaskedImageModeling,
        TFDeiTModel,
    )
    from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import DeiTImageProcessor




class 			UpperCAmelCase_     :


  '''simple docstring'''







  def __init__(					self     :					Any     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					List[str]=13     ,				UpperCamelCase__     :					int=30     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					Any=3     ,				UpperCamelCase__     :					Optional[int]=True     ,				UpperCamelCase__     :					int=True     ,				UpperCamelCase__     :					str=32     ,				UpperCamelCase__     :					Tuple=2     ,				UpperCamelCase__     :					Optional[int]=4     ,				UpperCamelCase__     :					List[Any]=37     ,				UpperCamelCase__     :					Tuple="gelu"     ,				UpperCamelCase__     :					Optional[int]=0.1     ,				UpperCamelCase__     :					List[str]=0.1     ,				UpperCamelCase__     :					Optional[int]=10     ,				UpperCamelCase__     :					Dict=0.02     ,				UpperCamelCase__     :					Optional[int]=3     ,				UpperCamelCase__     :					Dict=None     ,				UpperCamelCase__     :					Optional[int]=2     ,				)     ->      Any:







    """simple docstring"""






    __magic_name__								=			parent
    __magic_name__								=			batch_size
    __magic_name__								=			image_size
    __magic_name__								=			patch_size
    __magic_name__								=			num_channels
    __magic_name__								=			is_training
    __magic_name__								=			use_labels
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_act
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			type_sequence_label_size
    __magic_name__								=			initializer_range
    __magic_name__								=			scope
    __magic_name__								=			encoder_stride

    # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
    __magic_name__								=			(image_size // patch_size) ** 2
    __magic_name__								=			num_patches + 2
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    __magic_name__								=			None
    if self.use_labels:
      __magic_name__								=			ids_tensor([self.batch_size]     ,				self.type_sequence_label_size       )

    __magic_name__								=			self.get_config()

    return config, pixel_values, labels
  def 				_lowercase				(					self     :					Tuple       )     ->      List[str]:







    """simple docstring"""






    return DeiTConfig(
        image_size=self.image_size     ,				patch_size=self.patch_size     ,				num_channels=self.num_channels     ,				hidden_size=self.hidden_size     ,				num_hidden_layers=self.num_hidden_layers     ,				num_attention_heads=self.num_attention_heads     ,				intermediate_size=self.intermediate_size     ,				hidden_act=self.hidden_act     ,				hidden_dropout_prob=self.hidden_dropout_prob     ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,				is_decoder=UpperCamelCase__     ,				initializer_range=self.initializer_range     ,				encoder_stride=self.encoder_stride     ,				)
  def 				_lowercase				(					self     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					str     ,				UpperCamelCase__     :					Optional[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			TFDeiTModel(config=UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(result.last_hidden_state.shape     ,				(self.batch_size, self.seq_length, self.hidden_size)       )
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]     ,				UpperCamelCase__     :					List[str]       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			TFDeiTForMaskedImageModeling(config=UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(
        result.reconstruction.shape     ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

    # test greyscale images
    __magic_name__								=			1
    __magic_name__								=			TFDeiTForMaskedImageModeling(UpperCamelCase__       )

    __magic_name__								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
    __magic_name__								=			model(UpperCamelCase__       )
    self.parent.assertEqual(result.reconstruction.shape     ,				(self.batch_size, 1, self.image_size, self.image_size)       )
  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					Any       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.type_sequence_label_size
    __magic_name__								=			TFDeiTForImageClassification(UpperCamelCase__       )
    __magic_name__								=			model(UpperCamelCase__     ,				labels=UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)       )

    # test greyscale images
    __magic_name__								=			1
    __magic_name__								=			TFDeiTForImageClassification(UpperCamelCase__       )

    __magic_name__								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
    __magic_name__								=			model(UpperCamelCase__     ,				labels=UpperCamelCase__       )
    self.parent.assertEqual(result.logits.shape     ,				(self.batch_size, self.type_sequence_label_size)       )





  def 				_lowercase				(					self     :					Any       )     ->      Any:







    """simple docstring"""






    __magic_name__								=			self.prepare_config_and_inputs()
    __magic_name__	,       __magic_name__	,       __magic_name__								=			config_and_inputs
    __magic_name__								=			{"""pixel_values""": pixel_values}
    return config, inputs_dict







@require_tf
class 			UpperCAmelCase_     (					_A    ,			_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						(
      (
          TFDeiTModel,
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
      )
      if is_tf_available()
      else ()
  )
  a__       			=						(
      {
          """feature-extraction""": TFDeiTModel,
          """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
      }
      if is_tf_available()
      else {}
  )

  a__       			=						False
  a__       			=						False
  a__       			=						False
  a__       			=						False
  def 				_lowercase				(					self     :					List[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			TFDeiTModelTester(self       )
    __magic_name__								=			ConfigTester(self     ,				config_class=UpperCamelCase__     ,				has_text_modality=UpperCamelCase__     ,				hidden_size=37       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[int]:







    """simple docstring"""






    self.config_tester.run_common_tests()
  @unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
  def 				_lowercase				(					self     :					Dict       )     ->      List[Any]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      self.assertIsInstance(model.get_input_embeddings()     ,				(tf.keras.layers.Layer)       )
      __magic_name__								=			model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(UpperCamelCase__     ,				tf.keras.layers.Dense       )       )
  def 				_lowercase				(					self     :					Any       )     ->      int:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __magic_name__								=			model_class(UpperCamelCase__       )
      __magic_name__								=			inspect.signature(model.call       )
      # signature.parameters is an OrderedDict => so arg_names order is deterministic
      __magic_name__								=			[*signature.parameters.keys()]

      __magic_name__								=			["""pixel_values"""]
      self.assertListEqual(arg_names[:1]     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]     ,				UpperCamelCase__     :					int=False       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			super()._prepare_for_class(UpperCamelCase__     ,				UpperCamelCase__     ,				return_labels=UpperCamelCase__       )

    if return_labels:
      if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call       ).parameters:
        del inputs_dict["labels"]

    return inputs_dict





  @slow
  def 				_lowercase				(					self     :					Dict       )     ->      Union[str, Any]:







    """simple docstring"""






    for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __magic_name__								=			TFDeiTModel.from_pretrained(UpperCamelCase__       )
      self.assertIsNotNone(UpperCamelCase__       )







def a__       (			):






  '''simple docstring'''





  __magic_name__								=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
  return image





@require_tf
@require_vision
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  @cached_property
  def 				_lowercase				(					self     :					int       )     ->      Optional[Any]:







    """simple docstring"""






    return (
        DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
        if is_vision_available()
        else None
    )





  @slow
  def 				_lowercase				(					self     :					Any       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )

    __magic_name__								=			self.default_image_processor
    __magic_name__								=			prepare_img()
    __magic_name__								=			image_processor(images=UpperCamelCase__     ,				return_tensors="""tf"""       )

    # forward pass
    __magic_name__								=			model(**UpperCamelCase__       )

    # verify the logits
    __magic_name__								=			tf.TensorShape((1, 1000)       )
    self.assertEqual(outputs.logits.shape     ,				UpperCamelCase__       )

    __magic_name__								=			tf.constant([-1.0266, 0.1912, -1.2861]       )

    self.assertTrue(np.allclose(outputs.logits[0, :3]     ,				UpperCamelCase__     ,				atol=1E-4       )       )



from __future__ import annotations

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if (direction == 1 and array[indexa] > array[indexa]) or (
      direction == 0 and array[indexa] < array[indexa]
  ):
    __magic_name__	,       __magic_name__								=			array[indexa], array[indexa]

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    for i in range(A_,	low + middle   ):
      comp_and_swap(A_,	A_,	i + middle,	A_   )
    bitonic_merge(A_,	A_,	A_,	A_   )
    bitonic_merge(A_,	low + middle,	A_,	A_   )

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  if length > 1:
    __magic_name__								=			int(length / 2   )
    bitonic_sort(A_,	A_,	A_,	1   )
    bitonic_sort(A_,	low + middle,	A_,	0   )
    bitonic_merge(A_,	A_,	A_,	A_   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Optional[Any]       			=  input('Enter numbers separated by a comma:\n').strip()
    __lowerCAmelCase     :	List[Any]       			=  [int(item.strip()) for item in user_input.split(',')]

    bitonic_sort(unsorted, 0, len(unsorted), 1)
    print('\nSorted array in ascending order is: ', end='')
    print(*unsorted, sep=', ')

    bitonic_merge(unsorted, 0, len(unsorted), 0)
    print('Sorted array in descending order is: ', end='')
    print(*unsorted, sep=', ')

